Probability machine-learning-based communication and operation optimization for cloud-based UAVs
- 21 Downloads
This paper proposes a smart machine-learning-based unmanned aerial vehicle (UAV) control system that optimizes UAV operations in real time. The purpose of this system is to increase the efficiency of UAVs that need to operate with limited resources. This can be accomplished by allowing the UAVs in flight to identify their current state and respond appropriately. The proposed system, which is developed based on “cloud robotics,” benefits from the powerful computational capabilities of cloud computing and can therefore calculate many types of information received from various sensors in real time to maximize the performance of the UAV control system. The system learn about normal situations when creating models. That is, preprocessing data that is correlated with a particular situation and modeling it with a “multivariate Gaussian distribution.” Once the model is created, the UAV can be used to analyze the current situation in real time during flight. Of course, it is possible to recognize the situation based on the traditional RULE or the latest LSTM. However, this is not an appropriate solution for UAV situations where irregularities are severe and unpredictable. In this paper, we succeeded in recognizing the UAV flight status in real time by the proposed method and succeeded in optimizing it by adjusting communication cycle based on a recognized situation. Based on the results of this study, we expect to be able to stabilize and optimize systems that are highly irregular and unpredictable. In other words, this system will be extended to learn about various situations and create a model. A reliable and efficient smart system can be designed by judging the situation comprehensively.
KeywordsUAV security Probability machine learning Anomaly detection Safety critical system
This work was supported by an Institute for Information and Communications Technology Promotion (IITP) grant funded by the Korean Government (MSIP) (R7118-16-1002, Development of Driving Computing System Supporting Real-time Sensor Fusion Processing for Self-Driving Car).
This paper was written as part of Konkuk University’s research support program for its faculty on sabbatical leave in 2017.
- 2.Jeong H-J et al (2014) Mathematical modeling of a multilayered drift-stabilization method for micro-UAVs using inertial navigation unit sensor. J Appl Math 2014:1–11Google Scholar
- 3.Mercado DA et al (2013) GPS/INS/optic flow data fusion for position and velocity estimation. In: 2013 International Conference on Unmanned Aircraft Systems (ICUAS). IEEEGoogle Scholar
- 5.Waharte S, Trigoni N (2010) Supporting search and rescue operations with UAVs. In: 2010 International Conference on Emerging Security Technologies (EST). IEEEGoogle Scholar
- 7.Yun BY, Lee J (2014) A study on application of the UAV in Korea for integrated operation with spatial information. J Korean Soc Geospat Inf Syst 22(2):3–9Google Scholar
- 9.Chandler PR, Pachter M, Rasmussen S (2001) UAV cooperative control. In: American Control Conference. Proceedings of the 2001, vol 1. IEEEGoogle Scholar
- 12.Li P, Garratt M, Lambert A (2015) Sensing and control of a quadrotor using a visual inertial fusion method. In: 2015 15th International Conference on Control, Automation and Systems (ICCAS). IEEEGoogle Scholar
- 13.Marques N, I. S. T. Student. Integrated architecture for vision-based indoor localization and mapping of a quadrotor micro-air vehicleGoogle Scholar
- 14.Jeong H-J, Ha Y-K (2013) Design and implementation of secure control architecture for unmanned aerial vehicles. Int J Smart Home 7(3):385–392Google Scholar
- 15.Lin R, Khalastchi E, Kaminka G (2010) Detecting anomalies in unmanned vehicles using the Mahalanobis distance. In: 2010 IEEE International Conference on Robotics and Automation (ICRA). IEEEGoogle Scholar
- 16.Rasmussen CE (2006) Gaussian processes for machine learningGoogle Scholar
- 18.Gavai G et al (2015) Supervised and unsupervised methods to detect insider threat from enterprise social and online activity data. J Wirel Mob Netw Ubiquitous Comput Dependable Appl (JoWUA) 6(4):47–63Google Scholar
- 19.Knapp DJ Communication system employing a network of power managed transceivers that can generate a clocking signal or enable data bypass of a digital system associated with each transceiver. US Patent No 6,763,060. 13 July 2004Google Scholar